The present embodiments relate to reconstructing a three-dimensional image dataset from two-dimensional projection images.
In the field of X-ray imaging, it is known to reconstruct higher-dimensional image datasets from lower dimensional projection images, where, for example, three-dimensional image datasets may be reconstructed from two-dimensional projection images. In conventional computed tomography, at least one X-ray source is rotated about the patient in a gantry for this purpose in order to record one-dimensional or two-dimensional projection images. It has, however, already been proposed for the concept also to be transferred to other X-ray devices (e.g., X-ray devices having a C-arm). In each case, the computed tomography uses ionizing radiation as the X-ray technology. The reduction of the quantity of ionizing radiation used for a CT scanner is advantageous both for the patient and for any personnel present (e.g., if the computed tomography is being deployed as part of a medical intervention, (to monitor the progress of an intervention)).
Hence, the use of collimators (e.g., diaphragms) that enable the field of view actually illuminated by X-ray radiation to be restricted to a volume of interest (VOI) inside the patient has also been proposed for computed tomography. The collimator thus reduces the X-ray dose significantly by using collimation to block (or at least severely attenuate) X-ray radiation in regions outside a predetermined volume of interest. This type of computed tomography is frequently referred to as VOI CT.
One problem with VOI CT is that the associated projection images are generally subjected to data truncation, which provides that, for example, parallel to the plane in which the scanning trajectory lies, the object to be scanned (e.g., the patient) is at least in part not fully mapped in the projection images. The consequence of this is that conventional image reconstruction algorithms no longer produce satisfactory results, since in the case of truncated projection images in which the image region, which is illuminated despite the collimator, shows the object truncated, the object is fully fluoroscoped in projection images oriented differently for this purpose. The information is thus inconsistent, since part of the attenuated region is not represented in the truncated projection images.
To resolve this problem, the proposal has been made, for example, in an article by F. Dennerlein and A. Maier, “Approximate truncation robust computed tomography—ATRACT”, Physics in Medicine and Biology, Vol. 58, p. 6133-6148, 2013, for the so-called ATRACT algorithm, which forms a suitable approach for the reconstruction of a volume of interest from truncated projection data (cf. the article by Y. Xia et al., “Towards Clinical Application of a Laplace Operator-based Region of Interest Reconstruction Algorithm in C-arm CT”, IEEE Transactions on Medical Imaging, Vol. 33, p. 593-606, 2014). Even with strong lateral data truncation the ATRACT algorithm may effectively reduce truncation artifacts and thus provides high-quality reconstructions without an explicit extrapolation or previous knowledge being required. Thus the ATRACT algorithm is suitable for clinical use. As the cited article by Y. Xia et al. shows, in comparison to techniques based on extrapolation (cf. J. Hsieh et al., “A novel reconstruction algorithm to extend the CT scan field-of-view”, Medical Physics, Vol. 31, No. 9, p. 2385-2391, 2004), significantly better results are achieved with the ATRACT algorithm.
The ATRACT algorithm is based on splitting the conventional ramp filter inside the well-known frame of the filtered back-projection (cf. the FDK algorithm in L. A. Feldkamp et al., “Practical cone-beam algorithm”, J. Opt. Soc. Am., Vol. 1, No. 6, p. 612-619, 1984) in a local and a nonlocal partial filter. As part of the local partial step of the filter step, a Laplace filter (e.g., a second derivation) is implemented by applying a Laplace operator. In the nonlocal substep of the filter step, a radon-based residual filtering is performed (cf. steps A and B in section 3.3. of the article by F. Dennerlein and A. Maier cited by reference herein). As an alternative to a radon-based residual filtering, a convolution is also possible. It is important that in the ATRACT algorithm the high-frequency spikes that occur at the truncation boundaries (e.g., the boundaries of the image region) after the Laplace filtering are removed. This removal of the high-frequency spikes takes place ultimately prior to the nonlocal residual filtering, consequently by a spike filter, but may, however, be integrated into the residual filter, in that an inner integration of the radon-based or convolution residual filter is restricted to the interior of the image region (cf. the formula (20) in the cited article by F. Dennerlein and A. Maier). This removal of the spikes provides that the ATRACT algorithm is robust in respect of the truncation, and prevents the well-known cupping/capping artifacts if the normal FDK algorithm is deployed with truncated image data.
Hitherto, most ATRACT examinations related to the reconstruction of volumes of interest (VOI) that are essentially located in the center of the object to be scanned (“centered case”) are provided. In experimental examinations, however, it has been found that during the reconstruction of volumes of interest that are not located in the center of the object (“noncentered case”), artifacts may arise if the ATRACT algorithm is applied. This may also occur if an intensity gradient occurs elsewhere in a plane parallel to the plane of the scanning trajectory.